Spatially referenced data is commonly used in the oil and gas industry and other geophysical and geologic industries. Vast quantities of spatially referenced data are continually sold, bought, exchanged, and managed by oil and gas companies, geophysical and geologic data brokers, and data managers.
Spatially referenced data includes, for example, geophysical seismic data and geologic well data. Spatially referenced data may define (1) a subject, such as seismic data or well data, and (2) a location of the subject with respect to the Earth. The usefulness of spatially referenced data may depend on being able to identify the subject and the location. Further, determining coordinates for the data may be insufficient to determine the location, because there are many different systems, or datums, of latitude and longitude and projections of eastings and northings.
Some types of information which may be needed in meaningfully organizing and utilizing coordinate data include coordinate identities, geodetic identity, projection identity, and coordinate reference system parameters.
“Coordinate identities” imply either geographic coordinate reference systems (latitudes and longitudes) or projected coordinate reference systems (eastings and northings; aka X and Y values). The association of a coordinate identity with a set of coordinates is known as “geodetically classifying” the coordinates.
“Geodetic identity” is the metadata (being document data about data elements or attributes and data about records or data structures) which describes what system of coordinates are being used (e.g. North American datum of 1927 vs. World Geodetic System of 1984) and refers to latitude, longitude.
“Projection Identity” is used for easting and northings, and it would be the metadata which describes those (e.g. Universal Transverse Mercator Zone 16 vs. Louisiana South Lambert etc.).
“Coordinate reference system parameters” are those metadata which mathematically through provided parameters describe the various systems or which more accurately define systems which cannot be described explicitly by name such as North American Datum of 1927, World Geodetic System of 1984, Lambert State Plane Texas North or Universal Transverse Mercator Zone 15. Systems such as Transverse Mercator, Polyconic, Rectified Skew Orthomorphic, for instance, would need mathematic parameters with which to describe the coordinate identity.
Today, most spatially referenced data is stored electronically in defined file formats. Many file formats, such as UKOOA, SPS, and WITSML, include both coordinate data and coordinate identities. However, many formats have minimal or no coordinate identity information, making those formats more difficult to organize and use. FIG. 1 shows a typical data file providing minimal geodetic identity. Manually locating spatially referenced data, identifying the subject of the data, identifying the location of the subject, including the coordinate identities, and organizing the data for subsequent usage is a people and time intensive task.
Additionally, spatially referenced data was often recorded and updated manually, making it subject to human error. As a result, data was sometimes stored in a form inconsistent with the file format. Attempting to use misleading spatially referenced data can create many problems. For example, suppose correct seismic data was mistakenly associated with an incorrect system of reference. The seismic data may show a favorable probability of oil and gas in one area. However, because of the incorrect system of reference, the seismic data may be misinterpreted to show a different area nearby. FIG. 2 is a cartoon showing the impact of poorly or incorrectly referenced coordinates. The misinterpretation could cause significant loss of revenue, legal and ownership issues, environmental impact, and health and safety issues.
Properly organized and properly classified coordinate data yields many useful results. First, the coordinate data may be readily viewed on a mapping system or a graphical information system (GIS). FIG. 3 shows an example of seismic coordinate events posted on a map in the proper datum and projection versus the same data posted when improperly classified. Resulting comparisons indicate mislocation of approximately 400 feet. Second, the coordinate data may be quickly reprojected into another datum or projection. Third, the coordinate data may be translated into different formats and structures, such as WITSML, UKOOA, SPS, and SEG. Fourth, files and data that cannot readily be identified may become accessible to forensic analysis using other geodetic methods and tools. FIG. 4 shows well location data mapped with aerial imagery allowing the coordinate values to be compared to ground truth. Fifth, files may be grouped into geographic, project, or other archival schema methods. FIG. 5 shows an example of coordinate data files classified in such a manner. Sixth, unorganized data may be grouped in such a manner as to allow coordinate data to be loaded into industry processing systems, geospatial databases, and other systems requiring data in a geodetically identified and organized structure.
Thus, a need exists for automated identification and organization of spatially referenced data. A solution may lead to more accurate and more detailed descriptions of geophysical and geologic data, which in turn may lead to more effective location of minerals such as oil and gas.